Update app.py

app.py

@@ -1,9 +1,9 @@
 import tensorflow as tf
-import random
 from PIL import Image
 from tensorflow import keras
 import numpy as np
 import os
+import random
 import logging
 from tensorflow.keras.preprocessing import image as keras_image
 from huggingface_hub import from_pretrained_keras
@@ -15,16 +15,12 @@ logging.basicConfig(level=logging.INFO)
 
 class DiseaseDetectionApp:
     def __init__(self):
-      
-        self.class_names = [
-            "Normal",
-            "PNEUMONIA",
-        ]
-
-        keras.utils.set_random_seed(42)
-        self.model = from_pretrained_keras("ryefoxlime/PneumoniaDetection")
-        self.client=AzureOpenAI()
         
+        
+        self.class_names =['Normal', 'Pneumonia']
+        self.model =tf.keras.models.load_model("pneumonia_xray_prediction.keras")
+        self.client=AzureOpenAI()
+
 
     def predict_disease(self, image_path):
         """
@@ -37,36 +33,36 @@ class DiseaseDetectionApp:
         - predicted_disease: string
         """
         try:
-            # Load the image file, resizing it to the dimensions expected by the model
-            img = keras_image.load_img(image_path, target_size=(224, 224))  # Adjust target_size according to your model's expected input dimensions
-
-            # Convert the image to a numpy array
-            img_array = keras_image.img_to_array(img)
 
-            # Add an additional dimension to the array: (1, height, width, channels)
-            img_array = tf.expand_dims(img_array, 0)  # Model expects a batch of images, but we're only passing a single image
+          # Load the image file, resizing it to the dimensions expected by the model
+          img = keras_image.load_img(image_path, target_size=(256, 256))  # Adjust target_size according to your model's expected input dimensions
 
-            # Make predictions
-            predictions =self.model.predict(img_array)
+          # Convert the image to a numpy array
+          img_array = keras_image.img_to_array(img)
 
-            # Extract the predicted class and confidence
-            predict_class = self.class_names[np.argmax(predictions[0])]
-            # confidence = round(100 * np.max(predictions[0]), 2)
+          # Add an additional dimension to the array: (1, height, width, channels)
+          img_array = tf.expand_dims(img_array, 0)  # Model expects a batch of images, but we're only passing a single image
+          # print(img_array)
+          # Make predictions
+          predictions = self.model.predict(img_array)
 
-            return predict_class
+          # Extract the predicted class and confidence
+          predict_class =self.class_names[np.argmax(predictions[0])]
+          confidence = round(100 * np.max(predictions[0]), 2)
+          return predict_class
 
         except Exception as e:
             logging.error(f"Error predicting disease: {str(e)}")
             return None
 
     def classify_disease(self,image_path):
-      
+
           disease_name=self.predict_disease(image_path)
           print(disease_name)
-          if disease_name=="PNEUMONIA":
+          if disease_name=="Pneumonia":
             conversation = [
               {"role": "system", "content": "You are a medical assistant"},
-              {"role": "user", "content": f""" your task describe(classify) about the given disease as summary only in 3 lines.
+              {"role": "user", "content": f""" your task describe(classify) about the given disease as a summary only in 5 lines.
                         ```{disease_name}```
                 """}
                 ]
@@ -74,23 +70,20 @@ class DiseaseDetectionApp:
             response = self.client.chat.completions.create(
                 model="ChatGPT",
                 messages=conversation,
-                temperature=0,
+                temperature=0.4,
                 max_tokens=1000
             )
             # Get the generated topics message
-            
+
             result = response.choices[0].message.content
             return disease_name,result
 
           elif disease_name=="Normal":
             result="No problem in your xray image"
             return disease_name,result
-          
-          else:
-            logging.error("Error classify_disease disease")
-            return "Something went wrong"
 
-    def unzip_image_data(self):
+
+    def unzip_image_data(self,filespath):
         """
         Unzips an image dataset into a specified directory.
 
@@ -98,8 +91,8 @@ class DiseaseDetectionApp:
             str: The path to the directory containing the extracted image files.
         """
         try:
-          with ZipFile("image_dataset.zip","r") as extract:
-            directory_path="dataset_image"
+          with ZipFile(filespath,"r") as extract:
+            directory_path = random.randrange(100)
             extract.extractall(f"{directory_path}")
             return f"{directory_path}"
 
@@ -107,14 +100,14 @@ class DiseaseDetectionApp:
                 logging.error(f"An error occurred during extraction: {e}")
                 return ""
 
-    def example_images(self):
+    def example_images(self,filespath):
           """
           Unzips the image dataset and generates a list of paths to the individual image files and use image for showing example
 
           Returns:
               List[str]: A list of file paths to each image in the dataset.
           """
-          image_dataset_folder = self.unzip_image_data()
+          image_dataset_folder = self.unzip_image_data(filespath)
           image_extensions = ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp']
           image_count = len([name for name in os.listdir(image_dataset_folder) if os.path.isfile(os.path.join(image_dataset_folder, name)) and os.path.splitext(name)[1].lower() in image_extensions])
           example=[]
@@ -122,16 +115,25 @@ class DiseaseDetectionApp:
             for name in os.listdir(image_dataset_folder):
                 path=(os.path.join(os.path.dirname(image_dataset_folder),os.path.join(image_dataset_folder,name)))
                 example.append(path)
-          print()
+
           return example
-          
+
+    def get_example_image(self):
+      normal_image="Normal_dataset.zip"
+      tuberclosis_image="Pnemonia_dataset.zip"
+
+      normal_image_unziped=self.example_images(normal_image)
+      tuberclosis_image_unziped=self.example_images(tuberclosis_image)
+
+      return normal_image_unziped,tuberclosis_image_unziped
 
     def gradio_interface(self):
 
       with gr.Blocks(theme=gr.themes.Soft()) as demo:
         gr.HTML("""<center><h1>Pneumonia Disease Detection</h1></center>""")
 
-        exam_img=self.example_images()
+        normal_image,tuberclosis_image=self.get_example_image()
+
         with gr.Row():
           input_image =gr.Image(type="filepath",sources="upload")
           with gr.Column():
@@ -142,14 +144,26 @@ class DiseaseDetectionApp:
           button =gr.Button(value="Detect The Disease")
 
         button.click(self.classify_disease,[input_image],[output,classify_disease_])
+
         gr.Examples(
-        examples=exam_img,
+        examples=normal_image,
+        label="Normal X-ray Images",
         inputs=[input_image],
         outputs=[output,classify_disease_],
         fn=self.classify_disease,
+        examples_per_page=5,
         cache_examples=False)
-    
-        
+
+        gr.Examples(
+        examples=tuberclosis_image,
+        label="Pneumonia X-ray Images",
+        inputs=[input_image],
+        outputs=[output,classify_disease_],
+        examples_per_page=5,
+        fn=self.classify_disease,
+        cache_examples=False)
+
+
       demo.launch(debug=True)
 
 if __name__ == "__main__":